Abstract
Frequent itemset mining is a promising approach to the study of genomic profiling data. Here a dataset consists of real numbers describing the relative level in which a clone occurs in human DNA for given patient samples. One can then mine, for example, for sets of samples that share some common behavior on the clones, i.e., gains or losses. Frequent itemsets show promising biological expressiveness, can be computed efficiently, and are very flexible. Their visualization provides the biologist with useful information for the discovery of patterns. Also it turns out that the use of (larger) frequent itemsets tends to filter out noise.
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de Graaf, J.M., de Menezes, R.X., Boer, J.M., Kosters, W.A. (2005). Frequent Itemsets for Genomic Profiling. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_10
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DOI: https://doi.org/10.1007/11560500_10
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